基于符号时间序列的卫星图像分析--以阿尔及利亚地区为例
Satellite Images Analysis with Symbolic Time Series: A Case Study of the Algerian Zone
摘要：
卫星图像时间序列是研究土地占用及其演变的重要信息来源。事实上，存储的非常大量的数字数据通常不准备直接分析。时间序列数据采掘为了达到降维和信息抽取的双重目的，通常会引起时间序列表示的变化。在从表征变化中提取信息可理解性的目标中，我们可以使用时间序列的符号表征。许多高层次的时间序列表示已被提出用于数据采掘，包括傅立叶变换，小波，分段多项式模型等。许多研究人员也考虑了时间序列的符号表示，指出这种表示将潜在地允许研究人员利用丰富的数据结构和算法，从文本处理和生物信息学社区。在这项工作中，我们提出了一个主要的符号表示方法“SAX”（符号聚合近似），我们经历了这种方法来象征和减少在一段时间内获得的卫星图像时间序列的维数为5年的植被指数（NDVI）的演变特征。
英文摘要：
Satellite Image Time Series (SITS) are an important source of information for studying land occupation and its evolution. Indeed, the very large volumes of digital data stored, usually are not ready to a direct analysis. In order to both reduce the dimensionality and information extraction, time series data mining generally gives rise to change of time series representation. In an objective of information intelligibility extracted from the representation change, we may use symbolic representations of time series. Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. We present in this work, one of the main symbolic representation methods "SAX"(Symbolic Aggregate Approximation) and we experience this method to symbolize and reduce the dimensionality of a Satellite Image Times Series acquired over a period of 5 years by characterizing the evolution of a vegetation index (NDVI).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
